The main objective of creating a model(training data) is making sure it fits the data properly and reduce the loss. Sometimes the model that is trained which will fit the data but it may fail and give a poor performance during analyzing of data (test data). This leads to overfitting. Regularization came to overcome overfitting.

Lasso Regression (**Least Absolute Shrinkage and Selection Operator**) adds “Absolute value of magnitude” of coefficient, as penalty term to the loss function.

Lasso shrinks the less important feature’s coefficient to zero; thus, removing some feature altogether. So, this works well for feature selection in case we have a huge number of features.

Methods like Cross-validation, Stepwise Regression are there to handle overfitting and perform feature selection work well with a small set of features. These techniques are good when we are dealing with a large set of features.

Along with shrinking coefficients, the lasso performs feature selection, as well. (Remember the ‘selection‘ in the lasso full-form?) Because some of the coefficients become exactly zero, which is equivalent to the particular feature being excluded from the model.

**L2 Regularization(L2 = Ridge Regression)**

Overfitting happens when the model learns signal as well as noise in the training data and wouldn’t perform well on new/unseen data on which model wasn’t trained on.

To avoid overfitting your model on training data like **cross-validation sampling, reducing the number of features, pruning, regularization, **etc.

**So to avoid overfitting, we perform Regularization.**

**The Regression model that uses L2 regularization is called Ridge Regression.The formula for Ridge Regression:**

**Regularization adds the penalty as model complexity increases. The regularization parameter** **(lambda) penalizes all the parameters except intercept so that the model generalizes the data and** **won’t overfit.**

**Ridge regression adds “squared magnitude of the coefficient” as penalty term to the loss function. Here the box part in the above image represents the L2 regularization element/term.**

**Lambda is a hyperparameter.**

If lambda is zero, then it is equivalent to OLS. But if lambda is very large, then it will add too much weight, and it will lead to under-fitting.

Ridge regularization forces the weights to be small but does not make them zero and does not give the sparse solution.

Ridge is not robust to outliers as square terms blow up the error differences of the outliers, and the regularization term tries to fix it by penalizing the weights

Ridge regression performs better when all the input features influence the output, and all with weights are of roughly equal size.

L2 regularization can learn complex data patterns